Functional Testing
Without AI & With AI Integration
Without AI
01
Test Planning & Requirement Analysis
Objective: To understand and analyze functional requirements for the website to ensure all aspects are tested.
Steps: Review functional specifications or user stories to gather the test requirements.
Define scope, testable features, and test scenarios based on the functionality of the website.
Prioritize tests according to critical features and user impact.
02
Test Case Creation
Objective: To create a suite of test cases to ensure that each functionality of the website performs as expected.
Steps: Design test cases based on functional requirements.
Include a mix of positive and negative test scenarios to cover the intended and edge cases.
Define the expected results for each test scenario.
Consider different environments and browsers if cross-browser functionality is involved.
03
Test Execution
Objective: To run the test cases and validate that all functionalities are working correctly.
Steps: Execute the test cases manually or using automation tools (e.g., Selenium, QTP).
Test different workflows, from user registrations and logins to checkout processes.
Document the results and flag any discrepancies between expected and actual results.
04
Defect Reporting
Objective: To track and report any issues found during testing.
Steps: Log issues in bug tracking tools (e.g., Jira, Bugzilla).
Provide a detailed description of the issue, steps to reproduce, and screenshots if needed.
Categorize issues as critical, major, or minor to prioritize resolution.
05
Regression Testing
Objective: To ensure new updates or fixes don’t negatively affect existing functionality.
Steps: Rerun the previously executed functional test cases after updates or bug fixes.
Confirm that the new code did not break any existing features, particularly high-priority or core functionalities.
Test in different environments if applicable.
06
Sign-Off
Objective: To finalize the functional testing process and confirm readiness for release.
Steps: Review all test results and logs.
Ensure all high-priority defects are resolved.
Provide a detailed sign-off document confirming that the website’s functionalities are working as expected.
With AI
01
AI-Driven Test Planning
Objective: AI tools analyze past user interactions and historical data to define smarter testing strategies.
Steps: AI algorithms analyze user behavior data to prioritize testing high-risk or frequently used features.
Predict areas where failures are likely based on common failure points across similar websites or applications.
Automatically generate test plans based on historical data trends and the website’s evolving functionality.
02
AI-Assisted Test Case Creation
Objective: To leverage AI to automatically generate test cases based on requirements and historical data.
Steps: AI tools scan the website’s codebase and functional specifications to generate an optimal set of test cases.
Learn from past tests and automatically create new cases for changes or new features added to the website.
Suggest edge cases or test scenarios that may not have been considered manually.
03
Automated Test Execution with AI
Objective: To use AI to automate the execution of tests and dynamically adapt to changes in the website.
Steps: Run automated tests powered by AI tools (e.g., Selenium + AI).
AI optimizes the test execution flow, focusing on high-risk areas while minimizing redundant testing.
AI-driven test tools adjust based on any changes in UI/UX to ensure thorough coverage.
04
Intelligent Defect Reporting
Objective: AI automatically detects issues, prioritizes them based on severity, and suggests fixes.
Steps: AI tools categorize defects into different categories based on their impact, frequency, and severity.
AI automatically groups related issues and highlights high-priority problems first.
AI can suggest fixes or preventive measures based on historical defect data.
05
Predictive Regression Testing
Objective: AI predicts and performs regression testing based on the changes and areas most likely to be impacted.
Steps: AI models predict which areas of the website are most likely to be affected by recent code changes or additions.
The AI then suggests a targeted regression test suite, improving the efficiency of testing.
AI ensures that the most critical features are prioritized, saving time and resources.